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A conditional stochastic weather generator for seasonal to multi-decadal simulations

JOURNAL OF HYDROLOGY 2018年 556卷 835-846页

作者： Verdin, Andrew Rajagopalan, Balaji Kleiber, William Podesta, Guillermo Bert, Federico Univ Colorado Dept Civil Environm & Architectural Engn Boulder CO 80309 USA Univ Colorado Cooperat Inst Res Environm Sci Boulder CO 80309 USA Univ Colorado Dept Appl Math Boulder CO 80309 USA Univ Miami Sch Marine & Atmospher Sci Miami FL USA Univ Buenos Aires CONICET Fac Agron Buenos Aires DF Argentina

We present the application of a parametric stochastic weather generator within a nonstationary context, enabling simulations of weather sequences conditioned on interannual and multi-decadal trends. The generalized linear model framework of the weather generator allows any number of covariates to be included, such as large-scale climate indices, local climate information, seasonal precipitation and temperature, among others. Here we focus on the Salado A basin of the Argentine Pampas as a case study, but the methodology is portable to any region. We include domain-averaged (e.g., areal) seasonal total precipitation and mean maximum and minimum temperatures as covariates for conditional simulation. Areal covariates are motivated by a principal component analysis that indicates the seasonal spatial average is the dominant mode of variability across the domain. We find this modification to be effective in capturing the nonstationarity prevalent in interseasonal precipitation and temperature data. We further illustrate the ability of this weather generator to act as a spatiotemporal downscaler of seasonal forecasts and multidecadal projections, both of which are generally of coarse resolution. (C) 2015 Elsevier B.V. All rights reserved.

关键词： generalized linear models Stochastic weather generator Conditional simulation Downscaling seasonal forecasts Daily precipitation Daily temperature

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Detecting treatment differences in group sequential longitudinal studies with covariate adjustment

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BIOMETRICS 2018年第3期74卷 1072-1081页

作者： Jeffries, Neal O. Troendle, James F. Geller, Nancy L. NHLBI Off Biostat Res Bldg 10 Bethesda MD 20892 USA

In longitudinal studies comparing two treatments over a series of common follow-up measurements, there may be interest in determining if there is a treatment difference at any follow-up period when there may be a non-monotone treatment effect over time. To evaluate this question, Jeffries and Geller (2015) examined a number of clinical trial designs that allowed adaptive choice of the follow-up time exhibiting the greatest evidence of treatment difference in a group sequential testing setting with Gaussian data. The methods are applicable when a few measurements were taken at prespecified follow-up periods. Here, we test the intersection null hypothesis of no difference at any follow-up time versus the alternative that there is a difference for at least one follow-up time. Results of Jeffries and Geller (2015) are extended by considering a broader range of modeled data and the inclusion of covariates using generalized estimating equations. Testing procedures are developed to determine a set of follow-up times that exhibit a treatment difference that accounts for multiplicity in follow-up times and interim analyses.

关键词： generalized estimating equations generalized linear models Group sequential design Longitudinal analysis

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Assessing the impacts of seasonal and vertical atmospheric conditions on air quality over the Pearl River Delta region

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ATMOSPHERIC ENVIRONMENT 2018年 180卷 69-78页

作者： Tong, Cheuk Hei Marcus Yim, Steve Hung Lam Rothenberg, Daniel Wang, Chien Lin, Chuan-Yao Chen, Yongqin David Lau, Ngar Cheung Chinese Univ Hong Kong Dept Geog & Resource Management Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Inst Environm Energy & Sustainabil Hong Kong Hong Kong Peoples R China MIT Ctr Global Change Sci 77 Massachusetts Ave Cambridge MA 02139 USA Acad Sinica Res Ctr Environm Changes Taipei Taiwan

Air pollution is an increasingly concerning problem in many metropolitan areas due to its adverse public health and environmental impacts. Vertical atmospheric conditions have strong effects on vertical mixing of air pollutants, which directly affects surface air quality. The characteristics and magnitude of how vertical atmospheric conditions affect surface air quality, which are critical to future air quality projections, have not yet been fully understood. This study aims to enhance understanding of the annual and seasonal sensitivities of air pollution to both surface and vertical atmospheric conditions. Based on both surface and vertical meteorological characteristics provided by 1994-2003 monthly dynamic downscaling data from the Weather and Research Forecast Model, we develop generalized linear models (GLMs) to study the relationships between surface air pollutants (ozone, respirable suspended particulates, and sulfur dioxide) and atmospheric conditions in the Pearl River Delta (PRD) region. Applying Principal Component Regression (PCR) to address multi-collinearity, we study the contributions of various meteorological variables to pollutants' concentration levels based on the loading and model coefficient of major principal components. Our results show that relatively high pollutant concentration occurs under relatively low mid-level troposphere temperature gradients, low relative humidity, weak southerly wind (or strong northerly wind) and weak westerly wind (or strong easterly wind). Moreover, the correlations vary among pollutant species, seasons, and meteorological variables at various altitudes. In general, pollutant sensitivity to meteorological variables is found to be greater in winter than in other seasons, and the sensitivity of ozone to meteorology differs from that of the other two pollutants. Applying our GLMs to anomalous air pollution episodes, we find that meteorological variables up to mid troposphere (similar to 700 mb) play an important ro

关键词： Air quality Climate change Trans-boundary air pollution Principal component regression generalized linear models

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Recent trends in the frequency and duration of global floods

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EARTH SYSTEM DYNAMICS 2018年第2期9卷 757-783页

作者： Najibi, Nasser Devineni, Naresh CUNY City Coll Dept Civil Engn New York NY 10031 USA CUNY Ctr Water Resources & Environm Res City Water Ctr New York NY 10031 USA CUNY NOAA Cooperat Sci Ctr Earth Syst Sci & Remote Sensing New York NY 10031 USA

Frequency and duration of floods are analyzed using the global flood database of the Dartmouth Flood Observatory (DFO) to explore evidence of trends during 1985-2015 at global and latitudinal scales. Three classes of flood duration (i.e., short: 1-7, moderate: 8-20, and long: 21 days and above) are also considered for this analysis. The nonparametric Mann-Kendall trend analysis is used to evaluate three hypotheses addressing potential monotonic trends in the frequency of flood, moments of duration, and frequency of specific flood duration types. We also evaluated if trends could be related to large-scale atmospheric teleconnections using a generalized linear model framework. Results show that flood frequency and the tails of the flood duration (long duration) have increased at both the global and the latitudinal scales. In the tropics, floods have increased 4-fold since the 2000s. This increase is 2.5-fold in the north midlatitudes. However, much of the trend in frequency and duration of the floods can be placed within the long-term climate variability context since the Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation were the main atmospheric teleconnections explaining this trend. There is no monotonic trend in the frequency of short-duration floods across all the global and latitudinal scales. There is a significant increasing trend in the annual median of flood durations globally and each latitudinal belt, and this trend is not related to these teleconnections. While the DFO data come with a certain level of epistemic uncertainty due to imprecision in the estimation of floods, overall, the analysis provides insights for understanding the frequency and persistence in hydrologic extremes and how they relate to changes in the climate, organization of global and local dynamical systems, and country-scale socioeconomic factors.

关键词： Frequency Trend analysis Teleconnections Trends Floods Organizations Social factors generalized linear models Climatic variability Ocean-atmosphere system Tropical environments Socio-economic aspects Climate change North Atlantic Oscillation Atmospheric forcing Socioeconomic data Framework Duration Flood frequency Frequency analysis Atlantic Oscillation Oscillations Socioeconomics Pacific Decadal Oscillation River discharge Climate Statistical models Hydrology Persistence

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Wald-based spatial scan statistics for cluster detection

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2018年 127卷 298-310页

作者： Liu, Ying Liu, Yawen Zhang, Tonglin Univ Int Business & Econ Sch Stat 730 Chengxin Bldg10 Huixin Dongjie Beijing 100029 Peoples R China Purdue Univ Dept Stat 250 N Univ St W Lafayette IN 47907 USA

The spatial scan test, which is often carried out by maximizing a likelihood ratio-based statistic over a collection of cluster candidates, is widely used in cluster detection and disease surveillance. As the likelihood ratio statistic may not be available if the exact distribution of the response variable is not specified, a Wald-based spatial scan approach is proposed. The idea is to construct a special explanatory variable for spatial clusters in the linear function of a statistical model. The spatial scan test is carried out by scanning the special explanatory variable over the collection of cluster candidates. An advantage is that the Wald-based spatial scan statistic can bridge spatial clusters and linear functions of statistical models. It can be easily combined with well-known statistical models beyond generalized linear models. It is expected that the proposed approach will have a great impact on cluster detection when the likelihood inference is intractable or unavailable. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Cluster detection generalized linear models Likelihood ratio Overdispersion Spatial scan statistics Wald-based statistics

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Statistical models for the number of successful cyber intrusions

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JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS METHODOLOGY TECHNOLOGY-JDMS 2018年第1期15卷 49-63页

作者： Leslie, Nandi O. Harang, Richard E. Knachel, Lawrence P. Kott, Alexander US Army Res Lab Adelphi Lab Ctr 2800 Powder Mill Rd Adelphi MD 20783 USA

We propose several generalized linear models (GLMs) to predict the number of successful cyber intrusions (or intrusions) into an organization's computer network, where the rate at which intrusions occur is a function of the following observable characteristics of the organization: (i) domain name system (DNS) traffic classified by their top-level domains (TLDs);(ii) the number of network security policy violations;and (iii) a set of predictors that we collectively call the cyber footprint that is comprised of the number of hosts on the organization's network, the organization's similarity to educational institution behavior, and its number of records on ***. In addition, we evaluate the number of intrusions to determine whether these events follow a Poisson or negative binomial (NB) probability distribution. We reveal that the NB GLM provides the best fit model for the observed count data, number of intrusions per organization, because the NB model allows the variance of the count data to exceed the mean. We also show that there are restricted and simpler NB regression models that omit selected predictors and improve the goodness-of-fit of the NB GLM for the observed data. With our model simulations, we identify certain TLDs in the DNS traffic as having a significant impact on the number of intrusions. In addition, we use the models and regression results to conclude that the number of network security policy violations is consistently predictive of the number of intrusions.

关键词： Domain name server traffic cyber risk generalized linear models negative binomial model principal component analysis managed security service provider regression

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Modelos não lineares e lineares generalizados para avaliação da germinação de sementes de milho e soja

Modelos não lineares e lineares generalizados para avaliaç...

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作者： Amorim, Deoclecio Jardim Universidade Estadual Paulista

Dentre as características mais estudadas na indústria de sementes e bancos de germoplasma, destaca-se o potencial fisiológico, tendo em vista que sementes de maior qualidade fisiológica permitem obter uma rápida e uniforme emergência das plântulas, e consequentemente o estabelecimento do estande. O objetivo dessa pesquisa foi avaliar a germinação de sementes de milho (Zea mays L.) e soja (Glycine max (L.) Merrill) por meio de modelos não lineares e lineares generalizados. Foram utilizadas as cultivares de milho: AS 1633 PRO3, 2B587 RR, 2A401PW, AL Bandeirante e BRS 4103, e de soja as cultivares: DS59716 IPRO, CD2737 RR, CD251 RR, CD2820 IPRO e CD2857 RR, ambas da safra 2016/17. Avaliou-se a germinação de 20 sementes com quatro repetições por cultivar por meio do teste de emissão da raiz primária (protrusão). A contagem das sementes germinadas foi efetuada em intervalos regulares de 6, 12 e 24 horas até 204 horas, adotando-se como critério de germinação a protrusão da raiz primária ≥ 2 mm. Os dados foram dispostos na forma de porcentagem acumulada ao longo do tempo e pela proporção de sementes viáveis em cada intervalo de tempo testado dado por uma sequência de ensaios de Bernoulli. Os dados de porcentagem acumulada ao longo do tempo foram modelados pelas curvas não lineares de Gompertz e função de Hill de quatro parâmetros e os dados de proporção foram avaliados por modelos lineares generalizados testando as funções ligação: Probit, Logit e Complemento Log Log. As cultivares de milho que apresentaram a maior velocidade de germinação foram: AL Bandeirante e BRS 4103. Para soja os melhores resultados foram observados para as cultivares CD251 RR e CD2737 RR. As metodologias corroboraram quanto à classificação da qualidade fisiológica das cultivares. A curva de Gompertz teve melhor ajuste e permitiu aplicações práticas para o estudo de germinação estabelecendo um novo parâmetro para comparação de diferentes lotes de sementes. Os modelos lineares generalizados constituem

关键词： Germinação Velocidade de germinação Modelos não lineares Modelos lineares generalizados Germination Speed of germination Non-linear models generalized linear models Dissertação de mestrado

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Analyzing thresholds and efficiency with hierarchical Bayesian logistic regression

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VISION RESEARCH 2018年 148卷 49-58页

作者： Houpt, Joseph W. Bittner, Jennifer L. Wright State Univ Dept Psychol Dayton OH 45435 USA US Air Force Res Lab 711 HPW-RHCV Washington DC 20330 USA Rutgers State Univ Dept Psychol New Brunswick NJ USA

Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences.

关键词： Ideal observer analysis Efficiency Threshold estimation Hierarchical Bayesian analysis generalized linear models

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Asymptotic null distribution of the modified likelihood ratio test for homogeneity in finite mixture models

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2018年 127卷 248-257页

作者： Wong, Tony S. T. Lam, Kwok Fai Zhao, Victoria X. City Univ Hong Kong Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China

Likelihood-based methods play a central role in statistical inference for parametric models. Among these, the modified likelihood ratio test is preferred in testing for homogeneity in finite mixture models. The test statistic is related to the maximum of a quadratic function under general regularity conditions. Re-parameterization is shown to have overcome the difficulty when linear independence is not satisfied. models with parameter constraints are also considered. The asymptotic null distribution of the test statistic is shown to have a chi-bar-squared distribution in both constrained and unconstrained cases. We extend the result to linear models and demonstrate that the chi-bar-squared distribution is also applicable. The general asymptotic result provides a much simpler testing procedure with an exact form of the asymptotic distribution compared to re-sampling approach in the literature. It also offers accurate p-value as shown in simulation. The results are checked by extensive simulation and are supplemented by a breast cancer data example. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Chi-bar-squared distribution Degeneration generalized linear models linear independence Negative definiteness

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Application of generalized linear models to Predict Semiconductor Yield Using Defect Metrology Data

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2011年第1期24卷 44-58页

作者： Krueger, Dana C. Montgomery, Douglas C. Mastrangelo, Christina M. Kansas State Univ Dept Management Manhattan KS 66506 USA Arizona State Univ Dept Ind Engn Tempe AZ 85287 USA Univ Washington Dept Ind Engn Seattle WA 98195 USA

Semiconductor yield modeling is essential to identify processing issues, improve quality, and meet customer demand. However, the massive amounts of data collected during the fabrication process and the number of historical models available make yield modeling a complex and challenging task. This paper presents a methodology to guide the practitioner in determining what data should be collected, integrated, and aggregated, followed by a modeling strategy to forecast yield using generalized linear models based on defect metrology data. This technique yields results at both the die and the wafer levels, significantly outperforms existing models found in the literature based on prediction errors, and identifies significant factors that can drive process improvement. This method also allows the nested structure of the process to be considered in the model, improving predictive capabilities and violating fewer assumptions. An example is presented to discuss this approach and to demonstrate the advantages of these models over the models of the past.

关键词： Defect modeling generalized linear models logistic regression semiconductor yield yield modeling

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